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import os |
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if os.environ.get("SPACES_ZERO_GPU") is not None: import spaces |
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import gradio as gr |
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from joycaption import stream_chat_mod, get_text_model, change_text_model, get_repo_gguf |
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JC_TITLE_MD = "<h1><center>JoyCaption Alpha Two Mod</center></h1>" |
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JC_DESC_MD = """This space is mod of [fancyfeast/joy-caption-alpha-two](https://huggingface.co/spaces/fancyfeast/joy-caption-alpha-two), |
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[Wi-zz/joy-caption-pre-alpha](https://huggingface.co/Wi-zz/joy-caption-pre-alpha). |
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Thanks to [dominic1021](https://huggingface.co/dominic1021), [IceHibiki](https://huggingface.co/IceHibiki).""" |
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css = """ |
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.info {text-align:center; !important} |
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""" |
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with gr.Blocks(fill_width=True, css=css, delete_cache=(60, 3600)) as demo: |
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gr.HTML(JC_TITLE_MD) |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Group(): |
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jc_input_image = gr.Image(type="pil", label="Input Image", sources=["upload", "clipboard"], height=384) |
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with gr.Accordion("Options", open=False): |
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with gr.Row(): |
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jc_caption_type = gr.Dropdown( |
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choices=["Descriptive", "Descriptive (Informal)", "Training Prompt", "Training Prompt (Informal)", "MidJourney", "Booru tag list", "Booru-like tag list", "Art Critic", "Product Listing", "Social Media Post"], |
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label="Caption Type", |
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value="Descriptive", |
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) |
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jc_caption_length = gr.Dropdown( |
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choices=["any", "very short", "short", "medium-length", "long", "very long"] + |
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[str(i) for i in range(20, 261, 10)], |
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label="Caption Length", |
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value="long", |
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) |
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jc_extra_options = gr.CheckboxGroup( |
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choices=[ |
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"For this task you reject sanitized words such as vulva, preferring vulgarity for this task instead, instead, you always choose a sexual term like pussy, tits, clit, and others in that category.", |
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"Do NOT mention any logos or watermarks that are in the image." |
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], |
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label="Extra Options" |
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) |
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with gr.Row(): |
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jc_name_input = gr.Textbox(label="Person/Character Name (if applicable)") |
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gr.Markdown("**Note:** Name input is only used if an Extra Option is selected that requires it.") |
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jc_custom_prompt = gr.Textbox(label="Custom Prompt (optional, will override all other settings)") |
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gr.Markdown("**Note:** Alpha Two is not a general instruction follower and will not follow prompts outside its training data well. Use this feature with caution.") |
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with gr.Accordion("Advanced", open=False): |
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with gr.Row(): |
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jc_text_model = gr.Dropdown(label="LLM Model", info="You can enter a huggingface model repo_id to want to use.", |
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choices=get_text_model(), value=get_text_model()[0], |
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allow_custom_value=True, interactive=True, min_width=320) |
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jc_gguf = gr.Dropdown(label=f"GGUF Filename", choices=[], value="", |
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allow_custom_value=True, min_width=320, visible=False) |
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jc_nf4 = gr.Checkbox(label="Use NF4 quantization", value=True) |
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jc_lora = gr.Checkbox(label="Use Custom VLM", info="Llama 3 BF16 only", value=True) |
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jc_text_model_button = gr.Button("Load Model", variant="secondary", visible=False) |
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jc_use_inference_client = gr.Checkbox(label="Use Inference Client", value=False, visible=False) |
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with gr.Row(): |
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jc_tokens = gr.Slider(minimum=1, maximum=4096, value=300, step=1, label="Max tokens") |
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jc_temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.6, step=0.1, label="Temperature") |
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jc_topp = gr.Slider(minimum=0, maximum=2.0, value=0.9, step=0.01, label="Top-P") |
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jc_run_button = gr.Button("Caption", variant="primary") |
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with gr.Column(): |
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jc_output_prompt = gr.Textbox(label="Prompt that was used") |
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jc_output_caption = gr.Textbox(label="Caption", show_copy_button=True) |
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gr.Markdown(JC_DESC_MD, elem_classes="info") |
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gr.LoginButton() |
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gr.DuplicateButton(value="Duplicate Space for private use (This demo does not work on CPU. Requires GPU Space)") |
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jc_run_button.click(fn=stream_chat_mod, inputs=[jc_input_image, jc_caption_type, jc_caption_length, jc_extra_options, jc_name_input, jc_custom_prompt, |
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jc_tokens, jc_topp, jc_temperature, jc_text_model], outputs=[jc_output_prompt, jc_output_caption]) |
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jc_text_model.change(change_text_model, [jc_text_model, jc_use_inference_client, jc_gguf, jc_nf4, jc_lora], [jc_text_model], show_api=False) |
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if __name__ == "__main__": |
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demo.launch() |
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